Fuzzy Model Identification for Intelligent Control of a Vehicle Speed Limit

The need to increase road safety is a major concern, with millions of road users and pedestrians being killed in traffic accidents each year. Static speed limit signs are conventionally used to assist motorists in safe selection of speeds. Although appropriate to use under near ideal conditions, such signs fail to provide accurate information on speed selection when traffic and environmental conditions are less than ideal. To develop a variable speed limit system that utilizes fuzzy control technology to identify speed limits appropriate for differing environmental conditions the problem of structure identification of a fuzzy model was studied. This paper presents a fuzzy rulebase combined controller, which is a fuzzy rule-based combination of linear controllers, for nonlinear systems subject to parameter uncertainties. The paper discusses potential benefits and limitations associated with the model. The main interest has been on building fuzzy relationship models that are expressed by set of fuzzy linguistic propositions derived from the experience of the specialists. The proposed model was tested on a set of experimental data and against specialist knowledge the comparison was satisfactory regarding the aims of the model. This system dynamically updates posted speed limits to better reflect prevailing traffic and environmental conditions.